NVIDIA提出Skill Lift新方法，评估智能体技能提升，与现有方法相比有显著优势，值得一读。
NVIDIA新论文探讨评估智能体技能，提出Skill Lift方法，通过对比加载与未加载技能的执行差异来衡量技能提升。研究基于145个真实技能案例，发现技能执行、行为检查和技能效率方面有显著提升。
Very interesting new paper from NVIDIA. (bookmark it) It takes a closer look at evaluating agent s...
Very interesting new paper from NVIDIA. (bookmark it) It takes a closer look at evaluating agent skills. Enterprise teams are starting to leverage shared skill libraries, and the review gate is typically a scanner that checks structure, style, and security. NVIDIA measured whether that gate predicts anything. Across 145 real skills from internal and public catalogs, structural scan scores correlate with LLM-judge quality at a Spearman rho of 0.14. ACES proposes Skill Lift instead. In other words, run the same task twice under the same model, sandbox, workspace, and scorer, once with the skill loaded and once without. Then you measure the difference in what the agent completed. They scored 947 paired cases from 58 production skills across four harnesses, normalizing trajectories into a shared Agent Trajectory Interchange Format, so results compare across harnesses. They fins that the largest process-metric gains appear in skill execution, behavior check, and skill efficiency. Paper: arxiv.org/abs/2608.20614 Track more trending AI papers in our academy: academy.dair.ai 💬 11 🔄 3 ❤️ 22 👀 2110 📊 15 ⚡